ABSTRACT:

Strong gravitational lensing provides fundamental insights into the
understanding of the dark matter distribution in massive galaxies,
galaxy clusters, and the background cosmology. Despite their
importance, few gravitational arcs have been discovered so far. The
urge for more complete, large samples and unbiased methods of
selecting candidates increases. Several methods for the automatic
detection of arcs have been proposed in the literature, but large
amounts of spurious detections retrieved by these methods force
observers to visually inspect thousands of candidates per square
degree to clean the samples. This approach is largely subjective and
requires a huge amount of checking by eye, especially considering
the actual and upcoming wide field surveys, which will cover
thousands of square degrees.
In this paper we study the statistical properties of the colours of
gravitational arcs detected in the 37 deg^2 of the
CFHTLS-Archive-Research Survey (CARS). Most of them lie in a
relatively small region of the (g'-r',r'-i') colour-colour
diagram. To explain this property, we provide a model that includes
the lensing optical depth expected in a LCDM cosmology that, in
combination with the sources' redshift distribution of a given
survey, in our case CARS, peaks for sources at redshift z~1. By
furthermore modelling the colours derived from the spectral energy
distribution of the galaxies that dominate the population at that
redshift, the model reproduces the observed colours well.
By taking advantage of the colour selection suggested by both data
and model, we automatically detected 24 objects out of 90 detected
by eye checking. Compared with the single-band arcfinder, this
multi-band filtering returns a sample complete to 83% and a
contamination reduced by a factor of ~6.5. New gravitational arc
candidates are also proposed.